| 摘 要: 针对基于BP神经网络的IDS技术收敛速度较慢，易陷入局部最优值、网络瘫痪，系统稳定性差等问题， 本文提出了基于PSO-BP神经网络的入侵检测技术优化算法。利用粒子群优化算法优化BP网络的权重,首先利用PSO算 法优化得到一个最优初始值，然后通过BP网络算法修正误差值，从而获得最优值。 |
| 关键词: 粒子群优化算法；神经网络；入侵检测 |
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中图分类号: TP301
文献标识码: A
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| 基金项目: 福建省中青年教师教育科研项目，应用型本科院校基于大数据背景下的智慧校园建设(JAT160613). |
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| An Intrusion Detection Technology Optimization Algorithm Based on the PSO-BP Neural Network |
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LEI Yufei,LIN Yumei
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( Quanzhou Institute of Information Engineering, Quanzhou 362000, China)
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| Abstract: Many problems are found in the IDS technology based on the traditional BP neural network,including low convergence speed,easily falling into the local optimal value,network paralysis,poor system stability,etc.This paper presents an intrusion detection technology optimization algorithm based on the PSO-BP neural network which optimizes the BP network weight with the Particle Swarm Optimization(PSO)algorithm.Firstly,an optimal initial value is obtained by using the PSO algorithm,and then the error value is corrected with the BP network algorithm to obtain the optimal value. |
| Keywords: Particle Swarm Optimization(PSO)algorithm;neural network;intrusion detection |
